Search for: All records

In mathematics tutoring, using appropriate instructional discursive strategies, called "talk moves'', is critical to support student learning. Training tutors in the appropriate use of talk moves is a key component of tutor development programs. However, tutor development at scale is a challenge. Recent research has shown that automatic talk moves classification of tutorial discourse can facilitate large-scale delivery of personalized talk moves feedback. In this paper, we build on this work and share our current progress using large language models to classify talk moves in transcripts of tutoring sessions. We report classification results from fine-tuned models, prompt optimization, and supervised embedding vectors classification. The fine-tuned strategy performed best, yielding better performance (.87 macro and .93 weighted f1 score in predicting expert labels) than the current state-of-the-art RoBERTa model. We discuss trade-offs across methods and models. 

Quantum networks have experienced rapid advancements in both theoretical and experimental domains over the last decade, making it increasingly important to understand their large-scale features from the viewpoint of statistical physics. This review paper discusses a fundamental question: how can entanglement be effectively and indirectly (e.g., through intermediate nodes) distributed between distant nodes in an imperfect quantum network, where the connections are only partially entangled and subject to quantum noise? We survey recent studies addressing this issue by drawing exact or approximate mappings to percolation theory, a branch of statistical physics centered on network connectivity. Notably, we show that the classical percolation frameworks do not uniquely define the network’s indirect connectivity. This realization leads to the emergence of an alternative theory called “concurrence percolation”, which uncovers a previously unrecognized quantum advantage that emerges at large scales, suggesting that quantum networks are more resilient than initially assumed within classical percolation contexts, offering refreshing insights into future quantum network design.